FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator

• URL: http://arxiv.org/abs/2508.21040v1
• Date: Thu, 28 Aug 2025 17:44:52 GMT
• Title: FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator
• Authors: Huynh Tong Dang Khoa, Dang Hoai Nam, Vo Nguyen Le Duy,
• Abstract summary: We propose FW-GAN, a one-shot handwriting synthesis framework that generates realistic, writer-consistent text from a single example.<n>Our generator integrates a phase-aware Wave-MLP to better capture spatial relationships while preserving subtle stylistic cues.<n> Experiments on Vietnamese and English handwriting datasets demonstrate that FW-GAN generates high-quality, style-consistent handwriting.
• Score: 4.94997283141601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Labeled handwriting data is often scarce, limiting the effectiveness of recognition systems that require diverse, style-consistent training samples. Handwriting synthesis offers a promising solution by generating artificial data to augment training. However, current methods face two major limitations. First, most are built on conventional convolutional architectures, which struggle to model long-range dependencies and complex stroke patterns. Second, they largely ignore the crucial role of frequency information, which is essential for capturing fine-grained stylistic and structural details in handwriting. To address these challenges, we propose FW-GAN, a one-shot handwriting synthesis framework that generates realistic, writer-consistent text from a single example. Our generator integrates a phase-aware Wave-MLP to better capture spatial relationships while preserving subtle stylistic cues. We further introduce a frequency-guided discriminator that leverages high-frequency components to enhance the authenticity detection of generated samples. Additionally, we introduce a novel Frequency Distribution Loss that aligns the frequency characteristics of synthetic and real handwriting, thereby enhancing visual fidelity. Experiments on Vietnamese and English handwriting datasets demonstrate that FW-GAN generates high-quality, style-consistent handwriting, making it a valuable tool for augmenting data in low-resource handwriting recognition (HTR) pipelines. Official implementation is available at https://github.com/DAIR-Group/FW-GAN

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